Anticipated changes in Alaska extreme precipitation

2021 ◽  
Keyword(s):  
Extreme Precipitation ◽  
Climate Models ◽  
Global Climate ◽  
Warm Season ◽  
Global Climate Models ◽  
Convective Precipitation ◽  
Precipitation Event ◽  
Geographical Regions ◽  
The Mean ◽  
Gfdl Model

Abstract Flooding from extreme precipitation can have major impacts on society in Alaska. Understanding how these extremes may change in the future is needed for better planning under climate change. Data on future changes in extreme precipitation over Alaska from dynamically downscaled output of two global climate models (GFDL and CCSM) were employed in this study. Threshold amounts for duration of the precipitation event (1 hour, 1 day and 30 day) and return intervals (2, 10, and 50 years) are evaluated and further downscaled onto NOAA Atlas 14. For each duration and return interval, the models’ fractional changes of threshold amounts are applied to the Atlas 14 estimates to remove the model bias. The threshold amounts for nearly all event durations and return intervals are projected to increase from present (1979-2005) amounts to higher values in later decadal periods (2020-2049, 2050-2079, and 2080-2099), and the percentage increases generally exceed the changes in the mean amounts. The percentage increases are comparable in the various geographical regions of Alaska, but the increases in the actual amounts are greatest in the wetter Southeast. While the downscaled GFDL model shows larger increases than the CCSM model in amounts for nearly all durations and return intervals, both models indicate that convective precipitation will become an increasingly greater fraction of the total precipitation during the warm season. The increase in the proportion of convective precipitation is consistent with the more rapid increase in extreme amounts than in mean amounts.

scholarly journals Global climatology and trends in convective environments from ERA5 and rawinsonde data

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Mateusz Taszarek ◽  
John T. Allen ◽  
Mattia Marchio ◽  
Harold E. Brooks
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Global Climate ◽  
Convective Available Potential Energy ◽  
Global Climate Models ◽  
Convective Precipitation ◽  
The Past ◽  
The Tropics ◽  
Rawinsonde Data

AbstractGlobally, thunderstorms are responsible for a significant fraction of rainfall, and in the mid-latitudes often produce extreme weather, including large hail, tornadoes and damaging winds. Despite this importance, how the global frequency of thunderstorms and their accompanying hazards has changed over the past 4 decades remains unclear. Large-scale diagnostics applied to global climate models have suggested that the frequency of thunderstorms and their intensity is likely to increase in the future. Here, we show that according to ERA5 convective available potential energy (CAPE) and convective precipitation (CP) have decreased over the tropics and subtropics with simultaneous increases in 0–6 km wind shear (BS06). Conversely, rawinsonde observations paint a different picture across the mid-latitudes with increasing CAPE and significant decreases to BS06. Differing trends and disagreement between ERA5 and rawinsondes observed over some regions suggest that results should be interpreted with caution, especially for CAPE and CP across tropics where uncertainty is the highest and reliable long-term rawinsonde observations are missing.

Forecast and Uncertainty Analysis of Extreme Precipitation in China from Ensemble of Multiple Climate Models

2021 ◽  
Author(s):  
Peng Deng ◽  
Jianting Zhu
Keyword(s):  
Extreme Precipitation ◽  
Flood Control ◽  
Climate Models ◽  
Bayesian Model Averaging ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Model Averaging ◽  
Statistical Distribution ◽  
Global Climate Models ◽  
Effective Measure

Abstract Global climate change is expected to have major impact on the hydrological cycle. Understanding potential changes in future extreme precipitation is important to the planning of industrial and agricultural water use, flood control and ecological environment protection. In this paper, we study the statistical distribution of extreme precipitation based on historical observation and various Global Climate Models (GCMs), and predict the expected change and the associated uncertainty. The empirical frequency, Generalized Extreme Value (GEV) distribution and L-moment estimator algorithms are used to establish the statistical distribution relationships and the multi-model ensemble predictions are established by the Bayesian Model Averaging (BMA) method. This ensemble forecast takes advantage of multi-model synthesis, which is an effective measure to reduce the uncertainty of model selection in extreme precipitation forecasting. We have analyzed the relationships among extreme precipitation, return period and precipitation durations for 6 representative cities in China. More significantly, the approach allows for establishing the uncertainty of extreme precipitation predictions. The empirical frequency from the historical data is all within the 90% confidence interval of the BMA ensemble. For the future predictions, the extreme precipitation intensities of various durations tend to become larger compared to the historic results. The extreme precipitation under the RCP8.5 scenario is greater than that under the RCP2.6 scenario. The developed approach not only effectively gives the extreme precipitation predictions, but also can be used to any other extreme hydrological events in future climate.

scholarly journals Fidelity of the Observational/Reanalysis Datasets and Global Climate Models in Representation of Extreme Precipitation in East China

2018 ◽  
Vol 32 (1) ◽  
pp. 195-212 ◽  
Author(s):  
Sicheng He ◽  
Jing Yang ◽  
Qing Bao ◽  
Lei Wang ◽  
Bin Wang
Keyword(s):  
Extreme Precipitation ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Yangtze River Basin ◽  
Global Climate Model ◽  
Global Climate Models ◽  
Global Ocean ◽  
East China ◽  
Cmip5 Models

AbstractRealistic reproduction of historical extreme precipitation has been challenging for both reanalysis and global climate model (GCM) simulations. This work assessed the fidelities of the combined gridded observational datasets, reanalysis datasets, and GCMs [CMIP5 and the Chinese Academy of Sciences Flexible Global Ocean–Atmospheric Land System Model–Finite-Volume Atmospheric Model, version 2 (FGOALS-f2)] in representing extreme precipitation over East China. The assessment used 552 stations’ rain gauge data as ground truth and focused on the probability distribution function of daily precipitation and spatial structure of extreme precipitation days. The TRMM observation displays similar rainfall intensity–frequency distributions as the stations. However, three combined gridded observational datasets, four reanalysis datasets, and most of the CMIP5 models cannot capture extreme precipitation exceeding 150 mm day−1, and all underestimate extreme precipitation frequency. The observed spatial distribution of extreme precipitation exhibits two maximum centers, located over the lower-middle reach of Yangtze River basin and the deep South China region, respectively. Combined gridded observations and JRA-55 capture these two centers, but ERA-Interim, MERRA, and CFSR and almost all CMIP5 models fail to capture them. The percentage of extreme rainfall in the total rainfall amount is generally underestimated by 25%–75% in all CMIP5 models. Higher-resolution models tend to have better performance, and physical parameterization may be crucial for simulating correct extreme precipitation. The performances are significantly improved in the newly released FGOALS-f2 as a result of increased resolution and a more realistic simulation of moisture and heating profiles. This work pinpoints the common biases in the combined gridded observational datasets and reanalysis datasets and helps to improve models’ simulation of extreme precipitation, which is critically important for reliable projection of future changes in extreme precipitation.

scholarly journals Mean and extreme precipitation over European river basins better simulated in a 25 km AGCM

2018 ◽  
Vol 22 (7) ◽  
pp. 3933-3950 ◽  
Author(s):  
Reinhard Schiemann ◽  
Pier Luigi Vidale ◽  
Len C. Shaffrey ◽  
Stephanie J. Johnson ◽  
Malcolm J. Roberts ◽  
...  
Keyword(s):  
Extreme Precipitation ◽  
Climate Models ◽  
Global Climate ◽  
River Basins ◽  
Global Climate Models ◽  
Sensitivity Experiment ◽  
Scale Parameters ◽  
The North ◽  
Resolution Model ◽  
High Resolution Model

Abstract. Limited spatial resolution is one of the factors that may hamper applications of global climate models (GCMs), in particular over Europe with its complex coastline and orography. In this study, the representation of European mean and extreme precipitation is evaluated in simulations with an atmospheric GCM (AGCM) at different resolutions between about 135 and 25 km grid spacing in the mid-latitudes. The continent-wide root-mean-square error in mean precipitation in the 25 km model is about 25  % smaller than in the 135 km model in winter. Clear improvements are also seen in autumn and spring, whereas the model's sensitivity to resolution is very small in summer. Extreme precipitation is evaluated by estimating generalised extreme value distributions (GEVs) of daily precipitation aggregated over river basins whose surface area is greater than 50 000 km2. GEV location and scale parameters are measures of the typical magnitude and of the interannual variability of extremes, respectively. Median model biases in both these parameters are around 10 % in summer and around 20 % in the other seasons. For some river basins, however, these biases can be much larger and take values between 50 % and 100 %. Extreme precipitation is better simulated in the 25 km model, especially during autumn when the median GEV parameter biases are more than halved, and in the North European Plains, from the Loire in the west to the Vistula in the east. A sensitivity experiment is conducted showing that these resolution sensitivities in both mean and extreme precipitation are in many areas primarily due to the increase in resolution of the model orography. The findings of this study illustrate the improved capability of a global high-resolution model in simulating European mean and extreme precipitation.

scholarly journals Improvement and evaluation of simulated global biogenic soil NO emissions in an AC-GCM

2010 ◽  
Vol 10 (6) ◽  
pp. 16007-16054 ◽  
Author(s):  
J. Steinkamp ◽  
M. G. Lawrence
Keyword(s):  
Land Cover ◽  
Atmospheric Chemistry ◽  
Climate Models ◽  
Global Climate ◽  
Fertilizer Application ◽  
Global Climate Models ◽  
Top Down ◽  
Future Attempt ◽  
Geographical Regions ◽  
No Emissions

Abstract. Soil biogenic NO emissions (SNOx) play important direct and indirect roles in chemical processes of the troposphere. The most widely applied algorithm to calculate SNOx in global models was published 15 years ago by Yienger and Levy (1995), was based on very few measurements. Since then numerous new measurements have been published, which we used to build up a atabase of field measurements conducted world wide covering the period from 1978 to 2009, including 108 publications with 560 measurements. Recently, several satellite based top-down approaches, which recalculated the different sources of NOx (fossil fuel, biomass burning, soil and lightning), have shown an underestimation of SNOx by the algorithm of Yienger and Levy (1995). Nevertheless, to our knowledge no general improvements of this algorithm have yet been published. Here we present major improvements to the algorithm, which should help to optimize the representation of SNOx in atmospheric-chemistry global climate models, without modifying the underlying principal or mathematical equations. The changes include: 1) Using a new up to date land cover map, with twice the number of land cover classes, and using annually varying fertilizer application rates; 2) Adopting the fraction of SNOx induced by fertilizer application based on our database; 3) Switching from soil water column to volumetric soil moisture, to distinguish between the wet and dry state; 4) Tuning the emission factors to reproduce the measured emissions in our database and calculate the emissions based on their mean value. These steps lead us to increased global yearly SNOx, and our total SNOx source ends up being close to one of the top-down approaches. In some geographical regions the new results agree better with the top-down approach, but there are also distinct differences in other regions. This suggests that a ombination of both top-down and bottom-up approaches could be combined in a future attempt to provide an even better calculation of SNOx.

scholarly journals MJO Teleconnections over the PNA Region in Climate Models. Part II: Impacts of the MJO and Basic State

2020 ◽  
Vol 33 (12) ◽  
pp. 5081-5101
Author(s):  
Jiabao Wang ◽  
Hyemi Kim ◽  
Daehyun Kim ◽  
Stephanie A. Henderson ◽  
Cristiana Stan ◽  
...  
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Boreal Winter ◽  
Westerly Jet ◽  
The Pacific ◽  
Southward Shift ◽  
Baroclinic Model ◽  
Strong Amplitude ◽  
The Mean

AbstractIn an assessment of 29 global climate models (GCMs), Part I of this study identified biases in boreal winter MJO teleconnections in anomalous 500-hPa geopotential height over the Pacific–North America (PNA) region that are common to many models: an eastward shift, a longer persistence, and a larger amplitude. In Part II, we explore the relationships of the teleconnection metrics developed in Part I with several existing and newly developed MJO and basic state (the mean subtropical westerly jet) metrics. The MJO and basic state diagnostics indicate that the MJO is generally weaker and less coherent and propagates faster in models compared to observations. The mean subtropical jet also exhibits notable biases such as too strong amplitude, excessive eastward extension, or southward shift. The following relationships are found to be robust among the models: 1) models with a faster MJO propagation tend to produce weaker teleconnections; 2) models with a less coherent eastward MJO propagation tend to simulate more persistent MJO teleconnections; 3) models with a stronger westerly jet produce stronger and eastward shifted MJO teleconnections; 4) models with an eastward extended jet produce an eastward shift in MJO teleconnections; and 5) models with a southward shifted jet produce stronger MJO teleconnections. The results are supported by linear baroclinic model experiments. Our results suggest that the larger amplitude and eastward shift biases in GCM MJO teleconnections can be attributed to the biases in the westerly jet, and that the longer persistence bias is likely due to the lack of coherent eastward MJO propagation.

Multimodel Analysis of the Water Vapor Feedback in the Tropical Upper Troposphere

2006 ◽  
Vol 19 (20) ◽  
pp. 5455-5464 ◽  
Author(s):  
Ken Minschwaner ◽  
Andrew E. Dessler ◽  
Parnchai Sawaengphokhai
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Sea Surface ◽  
Specific Humidity ◽  
Upper Troposphere ◽  
The Mean ◽  
Tropical Upper Troposphere

Abstract Relationships between the mean humidity in the tropical upper troposphere and tropical sea surface temperatures in 17 coupled ocean–atmosphere global climate models were investigated. This analysis builds on a prior study of humidity and surface temperature measurements that suggested an overall positive climate feedback by water vapor in the tropical upper troposphere whereby the mean specific humidity increases with warmer sea surface temperature (SST). The model results for present-day simulations show a large range in mean humidity, mean air temperature, and mean SST, but they consistently show increases in upper-tropospheric specific humidity with warmer SST. The model average increase in water vapor at 250 mb with convective mean SST is 44 ppmv K−1, with a standard deviation of 14 ppmv K−1. Furthermore, the implied feedback in the models is not as strong as would be the case if relative humidity remained constant in the upper troposphere. The model mean decrease in relative humidity is −2.3% ± 1.0% K−1 at 250 mb, whereas observations indicate decreases of −4.8% ± 1.7% K−1 near 215 mb. These two values agree within the respective ranges of uncertainty, indicating that current global climate models are simulating the observed behavior of water vapor in the tropical upper troposphere with reasonable accuracy.

scholarly journals Evaluation of global climate models in simulating extreme precipitation in China

2013 ◽  
Vol 65 (1) ◽  
pp. 19799 ◽  
Author(s):  
Tinghai Ou ◽  
Deliang Chen ◽  
Hans W. Linderholm ◽  
Jee-Hoon Jeong
Keyword(s):  
Extreme Precipitation ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models
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